Understanding Label Bias
Supervised learning is built on the assumption that training datasets accurately represent the environments in which models will operate. However, this assumption must often be revised, particularly in fairness-critical applications. Challenges arise when data scientists must select appropriate labels, as cultural, contextual, or individual differences can lead to inconsistencies or oversimplifications in labelling. These oversights can fail to capture meaningful distinctions between classes, creating a pathway for label bias.
Label bias refers to distortions introduced during the labelling process, often stemming from systemic discrimination, inaccuracies, or ambiguities. These issues cause the training data to diverge from the underlying distribution, compromising model fairness and performance. Addressing label bias is essential to ensure equitable and effective machine learning outcomes.
Example for Label Bias in Machine Learning
Consider a scenario where images are labelled as “wedding.” People familiar with Western culture may label only pictures featuring brides in white dresses and grooms in dark suits as “wedding.” However, images of Indian weddings, characterized by colourful attire and distinctive decorations, might not be recognized as weddings by the same individual. This reflects label bias, where cultural context influences labelling, resulting in underrepresentation or misclassification of certain groups or scenarios. (Fahse et al., 2021).
Design Approach for Mitigating Label Bias in ML
Tackling labelling bias requires a systematic, proactive approach. You can get started with these resources:
Free Resources for Aggregation Bias Mitigation
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Mitigate and resolve 15 Types of Bias specific to your project with detailed guidance from problem definition to model monitoring. Packed with practical methods, research-based strategies, and critical questions specific to your use case. Customised checklists and +75 design cards for every phase in the AI/ ML pipeline
Summary
This guidance emphasizes that label bias in machine learning models can be effectively mitigated through thoughtful design approaches and practical debiasing strategies. By identifying and addressing label bias early in the development process, stakeholders can take proactive steps to ensure the accuracy and fairness of the models. While proxy labels can often mask fairness violations, strategies like data re-weighting, fairness constraints, and iterative refinement of surrogates offer viable pathways to counteract bias, even when the true labels are difficult to observe.
Collaboration across teams—data scientists, domain experts, and decision-makers—is essential in ensuring that fairness remains a priority throughout the entire machine learning lifecycle. By adopting these design approaches and debiasing strategies, stakeholders can contribute to creating models that are not only accurate but also fair, reducing the risk of harm and increasing the trust placed in AI systems.
Sources
Fahse, T., Huber, V. and van Giffen, B., 2021. Managing bias in machine learning projects. In Innovation Through Information Systems: Volume II: A Collection of Latest Research on Technology Issues (pp. 94-109). Springer International Publishing.
Jiang, H. and Nachum, O., 2020, June. Identifying and correcting label bias in machine learning. In International conference on artificial intelligence and statistics (pp. 702-712). PMLR.
Mhasawade, V., D’Amour, A. and Pfohl, S.R., 2024, June. A Causal Perspective on Label Bias. In The 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1282-1294).
Zhang, Y., Li, B., Ling, Z. and Zhou, F., 2024, March. Mitigating label bias in machine learning: Fairness through confident learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 15, pp. 16917-16925).